How is Gemini 3.1 Flash Lite different from `google/gemini-3.1-flash-lite-preview`?

Gemini 3.1 Flash Lite is the general-availability release of the same efficiency tier in the Gemini 3.1 family. The preview entry remains in the catalog for teams pinned to the earlier identifier; the GA model is the recommended target for new production work.

What thinking levels does Gemini 3.1 Flash Lite support and how do they affect cost?

Four levels: `minimal`, `low`, `medium`, and `high`. Higher levels add reasoning compute that contributes to output token counts, so the choice trades off latency and per-request cost against quality on harder inputs.

Can I mix thinking levels across requests in the same application?

Yes. Set `thinkingLevel` per request in `providerOptions.google.thinkingConfig`. Routine requests can run at `minimal` while flagged hard cases run at `medium` or `high` without any architectural changes.

Does Gemini 3.1 Flash Lite support multimodal inputs?

Yes. Gemini 3.1 Flash Lite accepts text, images, audio, and documents as input within the 1M tokens context window and returns text output. Web search and implicit caching are available as runtime options.

How do I call Gemini 3.1 Flash Lite on AI Gateway?

Use the identifier `google/gemini-3.1-flash-lite` with the AI SDK, the OpenAI-compatible Chat Completions endpoint, the Responses API, or any other supported interface. AI Gateway handles provider routing, retries, and failover automatically.

How does Zero Data Retention work with Gemini 3.1 Flash Lite through AI Gateway?

Yes, Zero Data Retention is available for this model. Zero Data Retention is offered on a per-provider basis. See https://vercel.com/docs/ai-gateway/capabilities/zdr for details.

Google

Gemini 3.1 Flash Lite

Gemini 3.1 Flash Lite is the GA release of the efficiency tier in the Gemini 3.1 generation, with improvements in reasoning, multimodal understanding, agentic tool use, and long-context performance over 2.5 Flash Lite, plus four configurable thinking levels and a context window of 1M tokens.

View API reference

Input and output price: Prices from: Input $0.25, Output $1.50, Per 1M tokens
24h uptime: Loading AI Gateway uptime

Get API key Read the docs

import { streamText } from 'ai'

const result = streamText({
  model: 'google/gemini-3.1-flash-lite',
  prompt: 'Why is the sky blue?'
})

Read docs

Overview Playground Providers Uptime Status Throughput Latency API Similar About FAQ

Copy link to headingPlayground

Try out Gemini 3.1 Flash Lite by Google. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.

Gemini 3.1 Flash Lite

Copy link to headingProviders

Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.

Provider

Context	Max Output	Latency	Throughput	Input	Output	Cache	Web Search	Capabilities	ZDR	No Training	Regional Inference	Free Tier	Release Date

Google

Legal:Terms•Privacy

65K

0.6 s

295 tps

$0.25/M+2 more

$1.50/M+2 more

Read$0.03/M

$14/K+1 more

05/07/2026

Google Vertex AI

Legal:Terms•Privacy

65K

0.8 s

288 tps

$0.25/M+4 more

$1.50/M+4 more

Read$0.03/M

$14/K+1 more

05/07/2026

Getting started

Call Gemini 3.1 Flash Lite through AI Gateway with the AI SDK generateText and streamText functions, or through the OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages APIs by changing the base URL. AI Gateway authenticates the request and routes it to an available provider.

Install the AI SDK (pnpm add ai dotenv), create an API key from the API Keys page, and set it as AI_GATEWAY_API_KEY in your environment. Full setup is covered in the text generation quickstart.

index.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'google/gemini-3.1-flash-lite',
    prompt: 'Why is the sky blue?',
  });

  console.log(result.text);
}

main().catch(console.error);

Top-level parameters

The same Gemini 3.1 Flash Lite request in each API format AI Gateway supports.

top-level-params.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'google/gemini-3.1-flash-lite',
    system: 'You are a concise technical assistant.',
    prompt: 'Summarize the tradeoffs between static generation and SSR.',
    maxOutputTokens: 1024,
  });

  console.log(result.text);
}

main().catch(console.error);

Standard parameters like prompt, messages, temperature, and tools work as documented in the AI SDK docs. These are the parameters with model-specific behavior.

Parameter	Type	Required	Description
`model`	`string`	Yes	Model ID in the form `creator/model`, e.g. `google/gemini-3.1-flash-lite`. AI Gateway routes the request to an available provider.
`maxOutputTokens`	`number`	No	Hard cap on generated tokens. Gemini 3.1 Flash Lite supports up to `65,000` output tokens. Reasoning tokens count toward this limit.
`reasoning`	`'provider-default' \| 'none' \| 'minimal' \| 'low' \| 'medium' \| 'high' \| 'xhigh'`	No	Provider-agnostic reasoning effort, available in AI SDK 7 or later. Maps to the provider’s native reasoning configuration; reasoning settings under `providerOptions` take precedence when both are set. See the Reasoning section below.
`providerOptions`	`Record<string, JSONValue>`	No	AI Gateway routing options under `gateway`, plus any provider-native options under the provider’s own namespace — see the table below.

Input limits

Input	Formats	Sources	Max count	Max size	Limits
Text	—	—	—	—	Prompt and response share the 1M-token context window
Image	—	URL, base64, Uint8Array	—	—	Sent as image parts in messages; counts as input tokens
PDF	—	URL, base64, Uint8Array	—	—	Sent as file parts in messages; counts as input tokens

Provider options

Set AI Gateway routing options under providerOptions.gateway. For provider-specific options, pass them under the provider’s namespace as documented by the AI SDK.

Learn more in the AI SDK google provider docs.

provider-options.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'google/gemini-3.1-flash-lite',
    prompt: 'Why is the sky blue?',
    providerOptions: {
      gateway: {
        only: ['google', 'vertex'],
      },
    },
  });

  console.log(result.text);
}

main().catch(console.error);

These AI Gateway routing options apply to every model. Provider-specific options pass through under the provider’s own namespace (for example providerOptions.anthropic) exactly as documented by the AI SDK.

Parameter	Type	Required	Description
`providerOptions.gateway.only`	`string[]`	No	Restrict routing to these provider slugs. Requests fail over only within the listed providers.
`providerOptions.gateway.order`	`string[]`	No	Preferred provider order. Listed providers are tried first; unlisted providers remain available as fallbacks.
`providerOptions.gateway.sort`	`'cost' \| 'ttft' \| 'tps'`	No	Rank candidate providers by price, time to first token, or tokens per second instead of the default routing order.
`providerOptions.gateway.zeroDataRetention`	`boolean`	No	Route only to providers with a zero-data-retention policy for this model.

Routing across providers

AI Gateway serves the same model through multiple providers and fails over automatically. order expresses a preference while keeping every provider eligible; only is a hard allowlist — if none of the listed providers are available the request fails instead of falling back.

Options under a provider's own namespace (for example providerOptions.anthropic) are forwarded to that provider with the request. Providers ignore option namespaces that don't apply to them, so it is safe to set provider options alongside gateway routing options.

Reasoning

AI Gateway bridges reasoning across every API format. The AI SDK exposes a provider-agnostic top-level reasoning level (none, minimal, low, medium, high, or xhigh); the Chat Completions and Responses formats take the same effort under reasoning.effort; and the Anthropic Messages format uses a native thinking token budget. Whichever you send, the gateway maps it to the target model’s native configuration, converting between effort levels and token budgets as needed. Reasoning-related settings under providerOptions take full precedence over the top-level reasoning value and are never merged. Reasoning tokens typically count toward your output-token usage, though how they’re reported and billed varies by provider.

Learn more in the AI Gateway reasoning guide.

reasoning.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'google/gemini-3.1-flash-lite',
    prompt: 'Explain the Monty Hall problem step by step.',
    reasoning: 'high',
  });

  console.log(result.text);
}

main().catch(console.error);

Image input

Send images alongside text as message parts. Images count as input tokens.

image-input.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'google/gemini-3.1-flash-lite',
    messages: [
      {
        role: 'user',
        content: [
          { type: 'text', text: 'Describe this image.' },
          { type: 'image', image: 'https://example.com/photo.jpg' },
        ],
      },
    ],
  });

  console.log(result.text);
}

main().catch(console.error);

PDF input

Attach PDFs as file parts. Their contents count as input tokens.

pdf-input.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'google/gemini-3.1-flash-lite',
    messages: [
      {
        role: 'user',
        content: [
          { type: 'text', text: 'Summarize this document.' },
          {
            type: 'file',
            mediaType: 'application/pdf',
            data: 'https://example.com/document.pdf',
          },
        ],
      },
    ],
  });

  console.log(result.text);
}

main().catch(console.error);

Tool calling

Expose tools the model can call. Define each tool’s inputs with a Zod schema.

tool-calling.ts

import { generateText, tool } from 'ai';
import { z } from 'zod';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'google/gemini-3.1-flash-lite',
    prompt: 'What is the weather in San Francisco?',
    tools: {
      getWeather: tool({
        description: 'Get the current weather for a location',
        inputSchema: z.object({ location: z.string() }),
        execute: async ({ location }) => ({ location, temperatureC: 18 }),
      }),
    },
  });

  console.log(result.text);
}

main().catch(console.error);

Copy link to headingMore models by Google

Model

Context	Latency	Throughput	Input	Output	Cache	Web Search	Capabilities	Providers	ZDR	No Training	Free Tier	Release Date

google/gemini-3.8-flash

1.7 s

404 tps

$0.75/M

$3.75/M

Read$0.08/M

$14/K+1 more

09/02/2026

google/gemini-3.7-flash

0.8 s

270 tps

$0.75/M

$3.75/M

Read$0.08/M

$14/K+1 more

08/13/2026

google/gemini-3.5-flash-lite

0.5 s

422 tps

$0.30/M

$2.50/M

Read$0.03/M

$14/K+1 more

07/21/2026

google/gemini-3.5-flash

1.5 s

187 tps

$1.50/M

$9/M

Read$0.15/M

$14/K+1 more

05/19/2026

google/gemini-3-flash

0.6 s

180 tps

$0.50/M+1 more

$3/M+1 more

Read$0.05/M

$14/K+1 more

12/17/2025

google/gemini-2.5-flash-lite

0.3 s

470 tps

$0.10/M

$0.40/M

Read$0.01/M

$35/K+1 more

06/17/2025

Copy link to headingAbout Gemini 3.1 Flash Lite

Gemini 3.1 Flash Lite is the general-availability version of the efficiency tier in the Gemini 3.1 generation, released May 7, 2026. It outperforms Gemini 2.5 Flash Lite on overall quality and lands close to 2.5 Flash performance across key capability areas, including reasoning, multimodal understanding, agentic tool use, and long-context performance.

The model is positioned for high-volume use cases where unit economics, not peak capability, set the constraint. Gemini 3.1 Flash Lite accepts text, images, audio, and documents as input within 1M tokens and produces text output, with implicit caching and web search available as runtime options to control cost and ground responses in current information.

Four configurable thinking levels (minimal, low, medium, high) allow a single deployment to serve mixed workloads without routing across models. A bulk extraction job can run at minimal to minimize latency and tokens; an edge case in the same pipeline can step up to medium or high when more deliberation pays off. Thinking tokens contribute to output token counts, so the level becomes a direct lever on cost.

Compared to the preceding preview release, Gemini 3.1 Flash Lite is the stable surface for production deployments. Teams running 2.5 Flash Lite at scale get the 3.1 generation quality gains on the workloads that drive the most tokens, including translation, data extraction, and code completion, without moving up to the standard Flash tier.

Copy link to headingWhat To Consider When Choosing a Provider

Configuration: Gemini 3.1 Flash Lite exposes four thinking levels through providerOptions.google.thinkingConfig: minimal, low, medium, and high. Lower levels favor latency and per-token cost; higher levels add reasoning compute that counts toward output tokens. Benchmark total cost under realistic thinking settings before committing to a deployment.
Zero Data Retention: Zero Data Retention is available for this model. It is offered on a per-provider and model basis. See the documentation for details.
Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.

Copy link to headingWhen to Use Gemini 3.1 Flash Lite

Best for

High-volume agentic pipelines: Aggregate token cost is a binding constraint and per-step reasoning depth can be tuned per request
Data extraction at scale: Structured extraction over millions of documents, invoices, transcripts, or scraped HTML where throughput economics dominate
Bulk translation workloads: Per-token cost determines whether the use case is viable, with thinking levels available for nuanced passages
Code completion and review: Inline suggestions, lint-style review, and refactor proposals at IDE or CI/CD scale
Multimodal ingestion at the lite tier: Vision and file inputs are required, but pro-tier latency and cost are not

Consider alternatives when

Maximum reasoning depth: Complex multi-step problems benefit from google/gemini-3.1-pro-preview or google/gemini-3-pro-preview
Native image output: Gemini 3.1 Flash Lite returns text only; google/gemini-3.1-flash-image-preview or google/gemini-3-pro-image generate images
Pro-grade quality at flash latency: google/gemini-3-flash sits between Flash Lite and Pro on capability and cost
Pure embedding workloads: Semantic retrieval and clustering fit a dedicated embedding model like google/gemini-embedding-001 better

Copy link to headingConclusion

Gemini 3.1 Flash Lite is the GA destination for teams that ran the 3.1 Flash Lite preview or are migrating from 2.5 Flash Lite. It brings the 3.1 generation quality lift to the highest-volume, most cost-sensitive workloads, with four thinking levels giving you a single deployment that adapts to mixed task difficulty without changing models.

Copy link to headingFrequently Asked Questions

How is Gemini 3.1 Flash Lite different from google/gemini-3.1-flash-lite-preview?
Gemini 3.1 Flash Lite is the general-availability release of the same efficiency tier in the Gemini 3.1 family. The preview entry remains in the catalog for teams pinned to the earlier identifier; the GA model is the recommended target for new production work.
How does Gemini 3.1 Flash Lite compare to Gemini 2.5 Flash Lite?
Gemini 3.1 Flash Lite outperforms 2.5 Flash Lite on overall quality and lands close to 2.5 Flash across reasoning, multimodal understanding, agentic tool use, and long-context performance. For teams already running 2.5 Flash Lite at scale, it's a quality upgrade within the same lite tier.
What thinking levels does Gemini 3.1 Flash Lite support and how do they affect cost?
Four levels: minimal, low, medium, and high. Higher levels add reasoning compute that contributes to output token counts, so the choice trades off latency and per-request cost against quality on harder inputs.
Can I mix thinking levels across requests in the same application?
Yes. Set thinkingLevel per request in providerOptions.google.thinkingConfig. Routine requests can run at minimal while flagged hard cases run at medium or high without any architectural changes.
Does Gemini 3.1 Flash Lite support multimodal inputs?
Yes. Gemini 3.1 Flash Lite accepts text, images, audio, and documents as input within the 1M tokens context window and returns text output. Web search and implicit caching are available as runtime options.
How do I call Gemini 3.1 Flash Lite on AI Gateway?
Use the identifier google/gemini-3.1-flash-lite with the AI SDK, the OpenAI-compatible Chat Completions endpoint, the Responses API, or any other supported interface. AI Gateway handles provider routing, retries, and failover automatically.
How does Zero Data Retention work with Gemini 3.1 Flash Lite through AI Gateway?
Yes, Zero Data Retention is available for this model. Zero Data Retention is offered on a per-provider basis. See https://vercel.com/docs/ai-gateway/capabilities/zdr for details.

Your use is subject to Google's Terms & Privacy Policies.

Agent Stack

Core Platform

Tools

Learn

Build

Explore

Gemini 3.1 Flash Lite

Copy link to headingPlayground

Copy link to headingProviders

Copy link to headingUptime24 hours

Copy link to headingThroughput24 hours

Copy link to headingLatency24 hours

Getting started

Top-level parameters

Input limits

Provider options

Routing across providers

Reasoning

Image input

PDF input

Tool calling

Copy link to headingMore models by Google

Copy link to headingAbout Gemini 3.1 Flash Lite

Copy link to headingWhat To Consider When Choosing a Provider

Copy link to headingWhen to Use Gemini 3.1 Flash Lite

Best for

Consider alternatives when

Copy link to headingConclusion

Copy link to headingFrequently Asked Questions